Journal of Earth System Science最新文献

Abstract

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the ‘very high’ landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction.

Research Highlights

• A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier.

• Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide.

• The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95.

• The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022.

Wetlands are characterized by flow and environmental dispersion, which are necessary in the water management system. Contaminant transport in wetlands has a significant effect in the field of biology and environmental fluid dynamics for the conservation of fish and wildlife species, erosion prevention, and mainly recreation. When an instantaneous contaminant is released into a wetland, both boundary absorptions and wind have a significant impact on the contaminant dispersion process. The present study highlights the wind effects and boundary absorption on environmental dispersion and stream-wise mean concentration in a width-dominated flow for a shallow wetland. A multi-scale time period is considered to determine the dispersion model, which illustrates the contaminant transport process greatly affected by different ecological parameters like dispersion time, boundary absorption, tortuosity and vegetation drag. It is noted that the stream-wise concentration distribution is almost identical to the transversal concentration distribution due to tortuosity and bottom vegetation effect. It is noticed that the stream-wise concentration of contaminant gradually decreases due to the increase of vegetation parameters and tortuosity. The distribution of flow velocity is derived analytically from the momentum equation for different wind strengths. The effect of boundary absorption strength on stream-wise and mean concentration are discussed. Also, it is evident that the distribution of concentration is complex for boundary absorption under the effects of wind.

Spatiotemporal variations of biomass burning (BB) over the Indian region using satellite-based data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003–2021 are analyzed and studied. We have used fire products with a high confidence level (≥ 80%), which is free from false alarm fires. The total fire counts (TFC), fire radiative power (FRP), and burned area (BA) for different land use and land cover (LULC) types over six different regions, namely Central India (CI), Indian Gangetic Plain (IGP), North-East India (NEI), North India (NI), South India (SI) and West India (WI) are studied. The biomass burning shows spatial, seasonal and inter-annual variations. Within the regions, different hotspots are identified for cropland burning, forest burning, etc. It is observed that in the IGP and WI regions, burning activity shows bi-modal seasonal behaviour, which coincides with crop burning after harvesting seasons, while other regions show a single mode. Non-parametric long-term analysis in TFC and TFRP (derived by adding FRP of all the fire hotspots in a respective year) shows a positive trend over all the regions except in the NEI region. The decreasing TFC with increasing precipitation is also observed in all the considered regions, which is attributed to enhanced moisture and decreased temperature. The present study provides the scientific basis for addressing the origin and type of biomass burning in different regions of India, and it is quite useful for developing procedures, awareness, and planning for reducing BB, which is quite harmful to human health as well as the environment.

Landslides are among the most dangerous and catastrophic natural hazards with countless concerns. In disaster rescue operations, fast and precise identification of landslides is necessary for timely and effective preventive actions. The landslide risk is anticipated to be reduced through their prediction, monitoring, and accurate detection using remote sensing technology. Moreover, deep learning algorithms have shown excellent improvement in various remote sensing applications. Recent scientific and intelligent technological innovations are needed to be applied to disaster management and assessment, particularly landslides. Therefore, this study aims to extract the landslide hazard information from multiple data sources, i.e., satellite and unmanned aerial vehicle (UAV) images, using a single staged object detection model, i.e., YOLOv5, YOLOv6, YOLOv7, and YOLOv8. The data from distinct platforms are utilized to infer the synergies between them. The results of each database are evaluated quantitatively using standard methods, i.e., precision, recall, f-score, and mean average precision, whereas visual analysis of results is conducted for qualitative assessment. Based on the experimental results, the highest f-score is represented by YOLOv7 (0.995) and YOLOv5 (0.921) for satellite and UAV-based data, respectively. The quantitative results are further compared with previous research work to exhibit the novelty and competence of the proposed research. Our work demonstrates the application and feasibility of the YOLO model in landslide information extraction for quick hazard recovery operations.

Liquefaction is one of the natural hazards that occurs due to earthquakes and has a significant impact on the loss of human lives and various civil infrastructures. In this study, metaheuristic ANN with optimization techniques (i.e., ANN-GWO, ANN-GTO, ANN-GAO, ANN-HHO, ANN-SSA, and ANN-SMA), machine learning techniques are used to predict the probability of liquefaction (({P}_{L})) from the SPT-based dataset. A dataset of 834 case histories, including seven geotechnical and seismic parameters, was used for training and testing different metaheuristic algorithms. The performance of the proposed machine learning algorithm used at every stage of analysis includes statistical parameters evaluation, score analysis, actual vs. predicted curve, error matrix, Taylor diagram, OBJ criteria, DDR criteria, and AIC criteria. The ANN-GTO model has been found to be the best model for the prediction of the probability of liquefaction potential of soil. However, all proposed models can successfully predict the liquefaction potential of soil with reasonably good accuracy. The proposed models can be used as a key tool in the prediction of the liquefaction susceptibility of any soil deposit.

The landform of the region highly influences the dynamics of the flood and plays a crucial role in directing the water flow, affecting the speed and volume of runoff. Assam, located in northeast India, experiences floods yearly due to adverse climatic conditions and complex terrain features. The objective of the present study is to understand the landform classification of Assam using the topographic position index (TPI) and geomorphon-based automated classification of landform (ACL) method and its spatial distribution with slope, geology, soil, LULC, and flood inundation. The ACL method shows that gentle slopes or flat areas occupy the maximum area ranging from 56.17 to 68.10% for TPI‐based slope position classes, and for geomorphon, slope feature occupies 20.61–25.39% of the total area. The spatial distribution of TPI and geomorphon-based landform classification was different because TPI compares the elevation of a point to the average elevation of its neighbourhood, while geomorphon classifies the landscape into predefined landform classes based on terrain shape and the spatial arrangement of elevation values. In both models, valleys are the most dominant landform class and are mainly present in the Central and Barak valley of Assam. The built-up areas and waterbodies on vulnerable landform classes increase their flood susceptibility. About 38.08% of the inundated area was found in wide valleys and 31% of the inundated area lies under flat landforms. The present study can be effective in land use planning, sustainable natural resource management, disaster risk management, and mitigation strategies.

Te–Bi bearing minerals are commonly present in many hydrothermal gold deposits and can provide important physicochemical constraints on their mineralization. The gold mineralization in the Xiejiagou gold deposit is hosted in the Mesozoic Linglong granite and consists of auriferous quartz veins and subordinate disseminated ores in the vein-proximal alteration zone. Three types of pyrite were identified that formed in stage I, II, and III. Gold occurs mostly as native gold and electrum (Ag > 20 wt.%) inclusions in or filling microfractures in pyrite. The abundant auriferous pyrite-quartz veins contain an assemblage of tsumoite and hessite. The tsumoite occurs as irregular inclusions in the Py₁ and is intergrown with the chalcopyrite. The hessite occurs as irregular inclusions or along the margins of the Py₂, and it coexists with the gold and galena. The tellurium fugacity continually decreased from stage I (log fTe₂ = −8.8 to −10.7) to stage III (log fTe₂ = −13.8 to −17.0). The sulfur fugacity increased from stage I (log fS₂ = −8.6 to −11.4) to stage II (log fS₂ = −7.2 to −11.4), and then, it decreased from stage II to stage III (log fS₂ = −9.8 to −13.0). These data indicate the conditions of the gold precipitation during the ore formation process. A detailed study of gold distribution in texturally different pyrite and the paragenetic association of tellurides provide valuable information on the distribution pattern of gold and in understanding the processes of gold deposition and evolution.

Subsurface characterization is a crucial aspect of geophysical exploration, enabling the identification and understanding of valuable geological bodies and resources. In this context, joint inversion of gravity and magnetic data has emerged as a powerful geophysical exploration technique, allowing for a more coherent and consistent interpretation of subsurface structures. The study focuses on understanding residual gravity and magnetic anomalies by employing the gradient descent-based joint inversion approach. A MATLAB program was developed to determine the inverse gravitational and magnetic anomalies using the gradient descent approach. We explored the potential of 2D rectangular prisms as a popular geometry to represent mineralized bodies and oil and gas structures. To overcome the non-uniqueness issues, we designed code for joint inversion of gravity and magnetic data. Synthetic data was inverted using the gradient descent technique and compared with the least-squares approach. Numerical simulations and real data application successfully reconstructed the geometry of the prisms. An illustrative example of a prism fault was used for further evaluation. Real data from the Oka complex in Quebec, Canada, was collected from the literature and subjected to joint and individual gravity and magnetic modelling. The results highlighted the influence of heterogeneous mass distribution on matching forward anomalies. The high gravity anomaly in the Oka complex was attributed to carbonatite and silicate rocks. The presence of two intrusive centres within the complex caused the magnetic high. This work demonstrates the effectiveness of the gradient descent approach as it consistently outperformed the conventional method, offering a robust solution for subsurface characterization in geophysical exploration.

Deep biospheric anaerobic microbial sulfate reduction and oxidative sulfur cycling have been studied in long sediment cores mainly acquired as part of IODP explorations. The most remarkable observation in many of these studies is the existence of an active sulfur cycle in the deep subsurface sediments that have very low organic carbon content and are presumably refractory. Here, we investigate the interstitial sulfate concentrations and sulfur isotope ratios in a 290 m-long sediment core collected from the eastern Arabian Sea at a water depth of 2663 m. Continuous decrease in porewater-sulfate concentrations with depth (up to 75 mbsf) coupled with enrichment in δ³⁴S_SO4 values suggests organoclastic sulfate reduction (OSR) processes attributed to the activity of sulfate-reducing bacteria (SRB) and retention of labile organic substrates amenable to the SRBs. Below a depth of 75 mbsf, the absence of further reduction in sulfate concentration indicates insufficient labile substrate to drive sulfate-reduction activity. An increase in sulfate concentrations at the deeper subsurface (below 128.5 mbsf) coupled with decreasing δ³⁴S_SO4 values may be attributed to the oxidation of Fe-sulfide to sulfate. The increase in porewater alkalinity in the lower part of the core has been linked to the silicate degradation process by CO₂ produced via the dissolution of CaCO₃. Compilation of previous studies from this core, along with our investigation, intrigues future research on organic matter reactivity and microbiological activity in deeper subsurface under oligotrophic depositional regimes.

Investigating the geochemical composition of bulk sediments stands as a crucial method for unraveling the complexities of various sedimentary processes. However, the intricacies arising from extensive datasets and alterations in sediment due to diverse factors often impede the clear identification of underlying patterns in geochemical fluctuations. In addressing these, employing multivariate statistical analyses has proven to be an invaluable tool for elucidating intricate patterns within large dataset. In this study, we focus on the utilization of Principal Component Analysis (PCA), a multivariate statistical technique, to uncover the underlying sedimentary processes influencing distinct geochemical dataset. Specifically, our attention is directed towards the examination of geochemical data from the previously published geochemical data of metasediments from Shimla and Chail group (referred to as SCM) and the mudflat sediments of Diu Island (referred to as DMS). Our PCA outcomes reveal that the initial three principal components (PC1, PC2, and PC3) account for 52.51% and 79.30% of the total variance within the SCM and DMS geochemical data, respectively. Notably, the negative loading of SiO₂, alongside positive loadings of incompatible elements and those associated with mafic rocks on PC1 within the SCM dataset, indicates sediment origins ranging from felsic to intermediate sources. Additionally, the coexistence of Th, U, Zr, and Sc, exhibiting positive loadings in PC1 and PC2, suggests a significant influence of reworking and recycling from felsic to intermediate sources. In the context of the DMS dataset, PCA analysis highlights the dominant influence of in-situ productivity and mafic sediment sources along the positive axis of PC1. Conversely, the negative axis of PC1 is shaped by intermediate and potentially other sources. Further granularity in interpretation reveals the positive axis of PC2 being attributed to weathering proxies, while the dominance of plagioclase minerals in the clayey fraction controls the positive axis of PC3. Through this investigation, our study underscores the essential role of PCA-assisted geochemical data analysis in unraveling the intricate web of processes contributing to the variance observed within sedimentary systems. By effectively distilling the multifaceted factors driving geochemical variability, this approach emerges as a pivotal asset in enhancing our understanding of sedimentary dynamics.